Developer Michiel Meire just released FastScheduler, a decorator-first Python task scheduler designed for simplicity. Frustrated by Celery's complexity for basic jobs, Meire built a tool that handles interval, daily, and cron-style tasks without requiring a message broker or separate workers. The library runs in-process, saving state to JSON files so scheduled jobs survive application restarts.

It supports async functions and timezone handling out of the box. For teams wanting visibility, an optional FastAPI dashboard provides real-time monitoring, execution history, and a dead letter queue for debugging failed jobs. FastScheduler fills a gap for developers who need reliable scheduling but find distributed task queues like Celery overkill for single-process applications.

The trade-off is clear: simplicity and ease of integration come at the cost of scalability. It's not designed for distributed worker fleets, but it's a compelling alternative for keeping lightweight background jobs close to the application code. Installation is modular, allowing users to add FastAPI integration or cron support as needed.